]]>https://cos.gmu.edu/cds/faculty-profile-olga-gkountouna/feed/0New Faculty Members for Fall 2019https://cos.gmu.edu/cds/new-faculty-members-for-fall-2019/
https://cos.gmu.edu/cds/new-faculty-members-for-fall-2019/#respondSat, 03 Aug 2019 16:07:21 +0000http://cos.gmu.edu/cds/?p=5333read more →]]>Please welcome to the Department of Computational and Data Sciences the following new faculty members:

You will have an opportunity to meet our new faculty at the Welcome Reception scheduled for Friday, August 30 from 2-4. More details to come.

]]>https://cos.gmu.edu/cds/new-faculty-members-for-fall-2019/feed/0NFL Combine Data Explorer by Carson Bulgin and William Wautlethttps://cos.gmu.edu/cds/nfl-visualization-project-by-carson-bulgin-and-william-wautlet/
https://cos.gmu.edu/cds/nfl-visualization-project-by-carson-bulgin-and-william-wautlet/#respondSat, 22 Jun 2019 23:11:42 +0000http://cos.gmu.edu/cds/?p=5315read more →]]>As a final project for Dr. Eagle’s CSI703 class, Carson Bulgin and William Wautlet created an excellent data visualization tool that compares college football players for the NFL draft. Their project allows users to examine relevant statistics between players and presents the data in a clean, well-organized interface through a series of radar charts, histograms, and parallel coordinates charts. Congratulations to Carson and William for a job well done!

Online social systems, comprised of social media services and platforms including social networking (e.g. Facebook, LinkedIn), microblogging (e.g. Twitter, Sina Weibo) and crowdsourcing (e.g. Wikipedia, OpenStreetMap) applications, continue to gain traction among an ever-increasing global user base. The growing reliance upon online social systems to augment an individual’s daily workflow and the resulting interdependence between human and technical systems provide sufficient evidence to classify them as socio-technical systems. These interdependencies are complex in nature and are best defined from a complex adaptive system (CAS) perspective.

It is through a CAS lens that this dissertation examines two types of adaptation in online social systems using an array of Computational Social Science (CSS) tools. In the first type of adaptation, human actors are no longer the sole participants in online social systems, since social bots, or automated software mimicking humans, have emerged as potential threats to stifle or amplify certain online conversation narratives. This section of the dissertation addresses adaptation to these new types of actors by presenting a novel social bot analysis framework designed to determine the pervasiveness and relative importance of social bots within various online conversations. In the second form of adaptation, individual citizens and government entities modify their behaviors in relation to each other through censorship circumvention or detection. This section of the dissertation investigates the rise of digital censorship in online social systems, creating a new agent-based model inspired by the findings from an evaluation of a Turkish digital censorship campaign.

The social bot analysis framework results consistently showed that while users identified as social bots only comprised a small portion of total accounts within the overall research corpus, they account for a significantly large portion of prominent centrality rankings across all observed online conversations. Furthermore, bot classification results, when using multiple bot detection platforms, exhibited minimal overlap, thus affirming that different bot detection algorithms focus on the various types of bots that exist. Finally, the results of the Turkish digital censorship campaign showed marginal effectiveness as some Turkish citizens circumvented the censorship policies, thus highlighting an individual decision cycle to risk punishment and engage in online activities. The recognition of this citizen decision cycle served as the basis for the adaptation to digital censorship model, which used empirical evidence to stylize and template a simulation censorship

This dissertation examines the integration of complexity theory and computational tools into U.S. foreign policy. It identifies ways to improve the Department of Defense’s main analytic framework to ensure a more accurate reflection of complex systems and it provides a holistic assessment of the integration of computational tools into Joint campaigns. Based on this analysis, this dissertation advocates the incorporation of Agent Based Models (ABMs) as simulations to support both analysis and foreign policy development at all levels of the foreign policy enterprise. To aid this integration two Mesa based ABM libraries are provided. (1) Multi-level Mesa, the first Python based multi-level library to facilitate the integration and evolution of layered adaptive networks. This library goes beyond existing multi-level libraries by providing greater user flexibility and allowing for the integration and adaption of more complex networks. (2) Distributed Space Mesa, a first attempt at starting a Distributed Mesa meta-library. This library provides modest time improvements to spatial Mesa ABMs and critical lessons for the continued development of a suite of distributed Mesa libraries.

]]>https://cos.gmu.edu/cds/oral-defense-of-doctoral-dissertation-computational-social-science-standardizing-complexity-doctrine-and-computation-for-integrated-campaigning-thomas-dietrich-pike/feed/0Department of Computational and Data Sciences Spring 2019 Doctoral Dissertationshttps://cos.gmu.edu/cds/department-of-computational-and-data-sciences-spring-2019-doctoral-dissertations/
https://cos.gmu.edu/cds/department-of-computational-and-data-sciences-spring-2019-doctoral-dissertations/#respondFri, 03 May 2019 18:01:09 +0000http://cos.gmu.edu/cds/?p=5295read more →]]>Congratulations to the following candidates for successfully defending their dissertations! A special thanks for all of the hard work that you have done and for the guidance of each Doctoral Committee. The Department wishes each of you the very best in your future endeavors!

Congratulations to Ajay Kulkarni, Computational Sciences and Informatics PhD student, who was recently acknowledged by the Mason Core Committee for teaching excellence. He received an outstanding review by the students he taught in CDS 102 – Intro Comp/Data Sciences Lab. He received above 4.75 out of 5 on the element “My overall rating of teaching” from the Student Ratings of Instruction. Great job, Ajay!